Andela Inc., one of the world’s largest marketplaces for technical talent, today announced the expansion of its new Andela AI Academy into a practical, production-focused upskilling program designed to prepare individuals and teams for enterprise-grade AI development and enable organizations to keep pace with rapid AI innovation.
Originally launched in partnership with GitHub to train developers on GitHub Copilot, the Andela AI Academy has rapidly expanded into multiple AI-focused learning tracks based on real enterprise needs. Building on the success of its initial program, Andela expects the AI Academy to train 15,000 technologists by 2026, creating the world’s largest just-in-time pipeline of AI-fluent, enterprise-ready engineering talent while also enabling organizations to upskill their existing teams. This dual approach, developing new AI-ready engineers and elevating in-house capabilities, is central to Andela’s vision of an AI-native talent platform that powers AI transformation globally.
“The speed of AI innovation is outpacing organizations’ ability to create value from it. Specifically in software development, teams are struggling to keep up with new tools, frameworks, and delivery patterns causing slower development cycles, rising costs, and stalled prototypes. Leaders know what they need to build—AI copilots, automation, RAG pipelines—but lack the internal fluency and execution confidence to make it real,” said Carrol Chang, Andela CEO. “The half-life of technical skills is shrinking, and learning has to happen continuously in the flow of work, tied to real business outcomes. Andela’s AI Academy will bring the engineering talent of the future to the forefront.”
This pioneering large-scale AI upskilling initiative will provide the training free to the 150,000+ technologists in the Andela network and through a training as a service model (TAAS) to organizations. It will concentrate on four core tracks:
- LLM Engineering – focusing on data science and GenAI foundations, this track provides the knowledge required for the AI Engineer role. Topics include RAG, prompt engineering, model evaluation, and working with open and closed-source models.
- Agentic AI Engineering – focusing on software engineering and application development, this track concentrates on agentic AI systems, orchestration, and tool use for commercial applications. It aims to equip learners to design, build, and deploy autonomous AI agents and abstract complex applications using orchestration frameworks.
- AI in Production – covering MLOps and production deployment, this track provides content on deployment, DevOps, MLOps, scalability, security, resilience, and observability. It teaches engineers how to ship real-world, production-grade AI systems across cloud platforms like AWS, GCP, and Azure, directly addressing enterprise-readiness requirements.
- AI Leadership – primarily non-technical, this track provides participants with the critical business perspective needed for industry readiness. It addresses domains such as AI strategy, commercial decision-making, and leadership, positioning the engineer to champion AI initiatives.
The focus areas are intended to drive faster prototyping, higher productivity, and better code quality, while embedding guardrails for secure, responsible AI adoption.
The first 80 participants have already completed a training program as Forward Deployed Engineers (FDEs). FDEs are expected to be both deeply technical and commercially minded so they can architect, deploy, and maintain production-grade systems. Another 200 participants completed a training program in AI Engineering. They moved beyond foundational knowledge into scalable agentic architectures, LLMOps, and real-world deployment patterns. The program is structured to build durable capability over time through iterative projects, peer reviews, and guided mentoring.
According to Asoluka Tochukwu Austin, of Nigeria, who finished the training, “The program really sharpened my ability to break down business problems and map them to the right GenAI approach — not just building something that “looks like AI,” but something that actually fits into an organization’s existing systems and delivers measurable value.”
Given the high demand for AI-fluent tech talent, Andela expects to place the 280 graduates in the job market very quickly with further academy cohorts to open for applications in February.
In addition to offering training to individuals in its network, Andela will train enterprise teams on the capabilities they’ll need to deliver AI features in production, prove progress with evidence, and keep skills current, so organizations can ship faster while building durable in-house capability for tomorrow.
Adds Chang, “The goal is for organizations to up-skill teams—never having to pause a roadmap—and maintain delivery momentum by pairing internal re-skilling with Andela’s AI-native talent, keeping projects on track while an organization’s own teams evolve into the next generation of AI-ready builders.”
Additional information about Andela’s Training as a Service offering is available here.
More about the AI Academy training program can be found here.
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